Choose your preferred view mode

Please select whether you prefer to view the MDPI pages with a view tailored for mobile displays or to view the MDPI
pages in the normal scrollable desktop version. This selection will be stored into your cookies and used automatically
in next visits. You can also change the view style at any point from the main header when using the pages with your
mobile device.

Abstract

Sewer condition is commonly assessed using closed-circuit television (CCTV) inspections. In this paper, we combine inspection results, pipe attributes, network data, and data on pipe environment to predict pipe condition and to discover which factors affect it. We apply the random forest algorithm to model pipe condition and assess the variable importance using the Boruta algorithm. We analyse the impact of predictor variables on poor condition using partial dependence plots, which are a valuable technique for this purpose. The results can be used in screening pipes for future inspections and provide insight into the dynamics between predictor variables and poor condition.
View Full-Text

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).